True Digital Control (PDF)
Statistical Modelling and Non-Minimal State Space Design
(Sprache: Englisch)
True Digital Control: Statistical Modelling and
Non-Minimal State Space Designdevelops a true digital
control design philosophy that encompasses data-based
model identification, through to control algorithm design,
robustness evaluation and...
Non-Minimal State Space Designdevelops a true digital
control design philosophy that encompasses data-based
model identification, through to control algorithm design,
robustness evaluation and...
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True Digital Control: Statistical Modelling and
Non-Minimal State Space Designdevelops a true digital
control design philosophy that encompasses data-based
model identification, through to control algorithm design,
robustness evaluation and implementation. With a heritage from both
classical and modern control system synthesis, this book is
supported by detailed practical examples based on the
authors' research into environmental, mechatronic and robotic
systems. Treatment of both statistical modelling and control design
under one cover is unusual and highlights the important connections
between these disciplines.
Starting from the ubiquitous proportional-integral
controller, and with essential concepts such as pole assignment
introduced using straightforward algebra and block diagrams, this
book addresses the needs of those students, researchers and
engineers, who would like to advance their knowledge of control
theory and practice into the state space domain; and academics who
are interested to learn more about non-minimal state variable
feedback control systems. Such non-minimal state feedback is
utilised as a unifying framework for generalised digital control
system design. This approach provides a gentle learning curve, from
which potentially difficult topics, such as optimal, stochastic and
multivariable control, can be introduced and assimilated in an
interesting and straightforward manner.
Key features:
* Covers both system identification and control system
design in a unified manner
* Includes practical design case studies and simulation
examples
* Considers recent research into time-variable and
state-dependent parameter modelling and control, essential
elements of adaptive and nonlinear control system design, and the
delta-operator (the discrete-time equivalent of the
differential operator) systems
* Accompanied by a website hosting MATLAB examples
True Digital Control: Statistical Modelling and
Non-Minimal State Space Design is a comprehensive and
practical guide for students and professionals who wish to further
their knowledge in the areas of modern control and system
identification.
Non-Minimal State Space Designdevelops a true digital
control design philosophy that encompasses data-based
model identification, through to control algorithm design,
robustness evaluation and implementation. With a heritage from both
classical and modern control system synthesis, this book is
supported by detailed practical examples based on the
authors' research into environmental, mechatronic and robotic
systems. Treatment of both statistical modelling and control design
under one cover is unusual and highlights the important connections
between these disciplines.
Starting from the ubiquitous proportional-integral
controller, and with essential concepts such as pole assignment
introduced using straightforward algebra and block diagrams, this
book addresses the needs of those students, researchers and
engineers, who would like to advance their knowledge of control
theory and practice into the state space domain; and academics who
are interested to learn more about non-minimal state variable
feedback control systems. Such non-minimal state feedback is
utilised as a unifying framework for generalised digital control
system design. This approach provides a gentle learning curve, from
which potentially difficult topics, such as optimal, stochastic and
multivariable control, can be introduced and assimilated in an
interesting and straightforward manner.
Key features:
* Covers both system identification and control system
design in a unified manner
* Includes practical design case studies and simulation
examples
* Considers recent research into time-variable and
state-dependent parameter modelling and control, essential
elements of adaptive and nonlinear control system design, and the
delta-operator (the discrete-time equivalent of the
differential operator) systems
* Accompanied by a website hosting MATLAB examples
True Digital Control: Statistical Modelling and
Non-Minimal State Space Design is a comprehensive and
practical guide for students and professionals who wish to further
their knowledge in the areas of modern control and system
identification.
Autoren-Porträt von C. James Taylor, Peter C. Young, Arun Chotai
James Taylor received his B.Sc. (Hons.) and Ph.D degreesfrom Lancaster University, UK, before joining the academic staff of
the Engineering Department in 2000. His research focuses on control
system design and system identification, with applied work spanning
robotics, transport, energy, agriculture and the environment. This
has led to over 100 publications in the open literature and
widespread impact across a variety of academic and
industry-based users. He has pioneered new advances in
non-minimal state space design, and coordinates development
of the well-known Captain Toolbox for Time Series Analysis
and Forecasting. He is a Fellow of the Institution of Engineering
and Technology, and supervises students across a spectrum of
mechanical, electronic, nuclear and chemical engineering
disciplines.
Peter Young is Emeritus Professor at Lancaster
University, UK, and Adjunct Professor at the Australian National
University, Canberra. After an apprenticeship in the Aerospace
Industry and B.Tech., MSc. degrees from Loughborough University, he
obtained his Ph.D degree from Cambridge University in 1970 and
became University Lecturer in Engineering and a Fellow of Clare
Hall at Cambridge University. After seven years as Professorial
Fellow at the Australian National University, he then moved to
Lancaster University in 1981 as Professor and Head of the
Environmental Science Department. He is well known for his work on
optimal identification, data-based mechanistic modelling and
adaptive forecasting, with applications in areas ranging from the
environment, through ecology, biology and engineering to business
and macro-economics.
Until his recent retirement, Arun Chotai was Senior
Lecturer in the Lancaster Environment Centre at Lancaster
University, UK. He holds a Ph.D in Systems and Control and a B.Sc.
(Hons.) in Mathematics, both from the University of Bath, UK.
Following his appointment to an academic position at Lancaster in
1984, he
... mehr
taught and developed modules in environmental systems,
courses that were then unique to the UK in providing an advanced,
quantitative approach to the subject. For many years, he was also
joint head (with present co-author Peter Young) of the
Systems and Control Group, which he helped to build into a
successful research unit that became known internationally for its
research in the areas of system identification, time-series
analysis and control system design.
courses that were then unique to the UK in providing an advanced,
quantitative approach to the subject. For many years, he was also
joint head (with present co-author Peter Young) of the
Systems and Control Group, which he helped to build into a
successful research unit that became known internationally for its
research in the areas of system identification, time-series
analysis and control system design.
... weniger
Bibliographische Angaben
- Autoren: C. James Taylor , Peter C. Young , Arun Chotai
- 2013, 1. Auflage, 360 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1118535502
- ISBN-13: 9781118535509
- Erscheinungsdatum: 28.05.2013
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Sprache:
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